Applications are welcomed for a 2-year post-doctoral position (with the possibility of further extension) in Computational Materials Discovery of Photovoltaic materials using Machine Learning Methods combined with abinitio modelling methods. The research will be carried out at RMIT University in Melbourne Australia under the direction of Professor Salvy Russo. This position is funded by the ARC Centre of Excellence in Exciton Science (ACEx), which is a newly established research centre, which will receive $31.85 million in funding by the Australian Research Council over the next 7 years.

This research centre links research teams at RMIT University, the University of Melbourne, Monash University, UNSW and the University of Sydney. Its partners include: The Defence Science and Technology Group, The Reserve Bank of Australia and CSIRO. The primary mission of ACEx is to manipulate the way light energy is absorbed, transported and transformed in advanced molecular materials. The Centre programmes span high-throughput computational screening, single molecule photochemistry and ultrafast spectroscopy and embrace innovative outreach and commercial translation activities. The Centre plans to capture the knowledge generated as new intellectual property, materials processing know-how, high-impact publications and through the creation of new employment opportunities. The expected outcomes and benefits include new Australian technologies in solar energy conversion, energy-efficient lighting and displays, security labelling and optical sensor platforms for defence.

Further details regarding the Centre of Excellence can be found at http://www.excitonscience.com

The appointee will join the RMIT Theoretical Chemical and Quantum Physics research group, with strong interactions with other research groups and institutions working under the ARC Centre of Excellence in Exciton Science (ACEx) and will work with Prof Salvy Russo’s team, focusing on materials discovery of new photovoltaic materials using machine learning methods and abinitio modelling techniques.

Job Requirements

The successful applicant will have a PhD in Physics, Computational/Theoretical Materials Science or Theoretical Physical Chemistry and research experience in the development and application of modern machine learning methods, combined with abintio modelling methods, applied to the prediction of electronic/optical properties of photovoltaic materials

The applicant should also be able to demonstrate:

• excellent mathematical and computer programming skills
• a solid background in applying machine learning methods to materials discovery
• An understanding of abinitio modelling methods such as DFT and TD-DFT or equivalent methods
• Experience in working with Large Material Science databases (such as Materials Project, AFLOW etc..) and/or working with organisations aligned to the Materials Genome Initiative.
• an ability to define, investigate and solve complex problems.
• Excellent written and oral communication skills
• the ability to work in a team
• a publication track record relevant to experience.

How to Apply

Applicants can lodge an application by following the instructions given in the following weblink:

http://yourcareer.rmit.edu.au/caw/en/job/575233/research-officer-in-machine-learning-computational-materials-discovery

A Position Description for the position can is attached to weblink.

Applicants must apply via the weblink given above, however please also send your CV and cover letter to [email protected]

The deadline for applications is 30th April 2019.

For further information please contact Prof Salvy Russo at [email protected]